Field
The present disclosure is generally related to artificial intelligence. More specifically, this disclosure relates to a method and system for detecting anomalous user activity by comparing the user's public and private activities and detecting changes in the correlation between the activities.
Related Art
Organizations have an interest in detecting when an employee may be acting against the interests of the organization. Some approaches to detecting such employee behavior include focusing on detecting anomalous insider activity by clustering users with a multi-domain probability model. These approaches detect anomalies by using clustering information to compare a user's activities with activities of other users in similar roles. Other approaches focus on estimating a person or group's intention to quit an organization, and may compute a score indicating an entity's intention to quit. However, these approaches may be insufficient for some organizations, and organizations may seek additional methods for detecting anomalous employee activity.